Vol.13, No.1, February 2024.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Analysis of Book Preferences Among Visitors in Library System


Zhi-Yao Foo, Kok-Why Ng, Su-Cheng Haw, Elham Abdulwahab Anaam


© 2024 Kok-Why Ng, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 13, Issue 1, Pages 422-430, ISSN 2217-8309, DOI: 10.18421/TEM131-44, February 2024.


Received: 24 September 2023.

Revised:   29 December 2023.
Accepted: 19 January 2024.
Published: 27 February 2024.




Library is a place that contains various resources and materials. Many invaluable knowledge can be found in the library. By analysing the library’s data, it is possible to obtain information that can further improve its services. This research aims to extract information from Multimedia University (MMU) library and present insightful visualization of the information to enhance the library administration. At present, the library does not have information on the book preferences of the library users. The book preferences statistics can be relatively helpful as the library will know what books can be imported in the future. By doing so, more people will visit the library and they will have more related books to use as reference or to read. In addition, there are no existing dashboards to display information on all borrowers, no visitor. In the absence of this, this research adopts the data science methodology to determine the book preferences of library users by using machine learning techniques such as clustering and classification. Lastly, a dashboard will be developed to display all the findings which includes statistics on the visitors and book preferences.


Keywords –Library, book preferences, analysis, recommender system, dashboard.



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